Automated processes exist for identifying users who tend to be early adopters of new items and technologies. These processes typically analyze the online behaviors of users, in conjunction with item release dates and item popularity data, to identify those who tend to purchase (or otherwise adopt) items before such items become popular. The actions of such early adopter users can be used by item recommendation systems to improve item recommendations, and by inventory management systems to more accurately predict and manage item inventory levels.
One problem with existing processes for identifying early adopter users is that they often fail to consider the degree to which particular users are influential. Another problem is that existing processes cannot distinguish between early adopter users and users who are not early adopter users, but who purchase some items upon release coincidentally.